1 rANOMALY step-by-step use case.

1.1 Help

Each function have a detailed help accessible in R via ?{funtion}.

1.2 Tests datasets

The dataset can be downloaded via this link.

This tutorial assume that you have extracted all the read file in a folder named reads along with the sample-metadata.csv file.

We share a 24 samples test dataset extract from rats feces at two different time (t0 & t50) and in two nutrition conditions. Also included two extraction control sample (blank).

sm <- read.table("sample_metadata.csv", sep="\t",header=TRUE)
DT::datatable(sm)
load("decontam_out/robjects.Rdata")

1.3 ASV definition with DADA2

The first step will be the creation of ASVs (Amplicon Sequence Variants) thanks to the dada2 package. In rANOMALY, only one function is needed to compute all the different steps require from this package.

Sample names will be extracted from the file name, so files must be formatted as followed : {sample-id1}_R1.fastq.gz {sample-id1}_R2.fastq.gz etc…

dada_res = dada2_fun(path="./reads", dadapool = "pseudo", compress=TRUE, plot=FALSE)

Main output: - read_tracking.csv that summarize the read number after each filtering step.

DT::datatable(read.table("dada2_out/read_tracking.csv",sep="\t",header=TRUE))

The sample names extracted from the file name. We consider as sample name anything that is before the first underscore. This must match the sample names that are in sample metadata files. input: raw read number. filtered: after dada2 filtering step: no N’s in sequence, low quality, and phiX. denoisedF & denoisedR: after denoising. Forward & Reverse. merged: after merging R1 & R2. nonchim: after chimeras filtering.

  • dada2_robjects.Rdata with raw ASV table and representative sequences in objects otu.table, seqtab.export & seqtab.nochim.
  • raw_asv-table.csv
  • rep-seqs.fna

1.4 Taxonomic assignment

This function uses IDTAXA function from DECIPHER package, and allows to use 2 differents databases. It keeps the best assignation on 2 criteria, resolution (depth) and confidence. The final taxonomy is validated by multiple ancestors taxa and incongruity correction step.

We share the latest databases we use in the IDTAXA format in this link. You can also generate your own database following those instructions and scripts we provide in another repository.

tax.table = assign_taxo_fun(dada_res = dada_res, id_db = c("path_to_your_banks/silva/SILVA_SSU_r132_March2018.RData","path_to_your_banks/DAIRYdb_v1.2.0_20190222_IDTAXA.RData") )

Main output: - taxo_robjects.Rdata with taxonomy in phyloseq format in tax.table object. - final_tax_table.csv the final assignation table that will be use in next steps. - allDB_tax_table.csv raw assignations from the two databases, mainly for debugging.

1.5 Phylogenetic Tree

The phylogenetic tree from the representative sequences is generated using phangorn and DECIPHER packages.

tree = generate_tree_fun(dada_res)

Main output: - tree_robjects.Rdata with phylogenetic tree object in phyloseq format.

1.6 Phyloseq object

To create a phyloseq object, we need to merge four objects and one file: - the asv table otu.table and the representative sequences seqtab.nochim from dada2_robjects.Rdata - a taxonomy table taxo_robjects.Rdata from taxo_robjects.Rdata - the phylogenetic tree tree from tree_robjects.Rdata - metadata from sample-metadata.csv

data = generate_phyloseq_fun(dada_res = dada_res, taxtable = tax.table, tree = tree, metadata = "./sample_metadata.csv")

Main output: - robjects.Rdata with phyloseq object in data for raw counts and data_rel for relative abundance.

1.7 Decontamination

The decontam_fun function uses decontam R package with control samples to filter contaminants. The decontam package offers two main methods, frequency and prevalence (and then you can combine those methods). For frequency method, it is mandatory to have the dna concentration of each sample in phyloseq (and hence in the sample-metadata.csv). “In this method, the distribution of the frequency of each sequence feature as a function of the input DNA concentration is used to identify contaminants.” In the prevalence methods no need of DNA quantification. “In this method, the prevalence (presence/absence across samples) of each sequence feature in true positive samples is compared to the prevalence in negative controls to identify contaminants.

Tips: sequencing plateforms often quantify the DNA before sequencing, but do not automaticaly give the information. Just ask for it ;).

Our function integrates the basics ASV frequency (nb_reads_ASV/nb_total_reads) and prevalence (nb_sample_ASV/nb_total_sample) filtering. As in our lab we had a known recurrent contaminant we included an option to filter out ASV based on they taxa names.

data = decontam_fun(data = data, domain = "Bacteria", column = "type", ctrl_identifier = "control", spl_identifier = "sample", number = 100)

Main output: - robjects.Rdata with contaminant filtered phyloseq object named data. - Exclu_out.csv list of filtered ASVs for each filtering step. - Kronas before and after filtering. - raw_asv-table.csv & relative_asv-table.csv. - venndiag_filtering.png.

venndiag

venndiag

1.8 Plots, diversity and statistics

!!! We are currently developping a ShinyApp to visualize your data, sub-select your samples/taxons and do all those analyses interactively !!! ExploreMetabar

1.8.1 Rarefaction curves

In order to observe the sampling depth of each samples we start by plotting rarefactions curves. Those plots are generated by Plotly which makes the plots interactive.

rarefaction(data, "souche_temps", 100 )
## rarefying sample SB1-Sauv0
## rarefying sample SB10-Mut0
## rarefying sample SB11-Mut0
## rarefying sample SB12-Mut0
## rarefying sample SB13-Sauv50
## rarefying sample SB14-Sauv50
## rarefying sample SB15-Sauv50
## rarefying sample SB16-Sauv50
## rarefying sample SB17-Sauv50
## rarefying sample SB18-Sauv50
## rarefying sample SB19-Mut50
## rarefying sample SB2-Sauv0
## rarefying sample SB20-Mut50
## rarefying sample SB21-Mut50
## rarefying sample SB22-Mut50
## rarefying sample SB23-Mut50
## rarefying sample SB24-Mut50
## rarefying sample SB3-Sauv0
## rarefying sample SB4-Sauv0
## rarefying sample SB5-Sauv0
## rarefying sample SB6-Sauv0
## rarefying sample SB7-Mut0
## rarefying sample SB8-Mut0
## rarefying sample SB9-Mut0
## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

1.8.2 Composition plots

Composition plots reveals here the top 10 genus present in our samples. #TODO Ord1 option control the… Fact1 option control the…

1.8.2.1 Relative abundance

bars_fun(data = data, top = 10, Ord1 = "souche_temps", Fact1 = "souche_temps", rank="Genus", relative = TRUE)

1.8.2.2 Raw abundance

bars_fun(data = data, top = 10, Ord1 = "souche_temps", Fact1 = "souche_temps", rank="Genus", relative = FALSE)

1.8.3 Diversity analyses

1.8.3.1 Alpha diversity

This function computes various alpha diversity indexes and returns

alpha <- diversity_alpha_fun(data = data, output = "./plot_div_alpha/", column1 = "souche", column2 = "temps",
                    column3 = "", supcovs = "", measures = c("Observed") )
## INFO [2020-08-19 11:42:44] Alpha diversity tab ...
## INFO [2020-08-19 11:42:44] Done.
## INFO [2020-08-19 11:42:44] Plotting ...
## INFO [2020-08-19 11:42:44] Done.
## INFO [2020-08-19 11:42:45] ANOVA ...
## INFO [2020-08-19 11:42:45] Done.
## INFO [2020-08-19 11:42:45] Finish.
1.8.3.1.1 Tables
DT::datatable(alpha$alphatable, filter = "top")

1.8.3.2 Beta diversity

beta <- diversity_beta_fun(data = data, output = "./plot_div_beta/", glom = "ASV", column1 = "temps", column2 = "souche", covar ="")
## INFO [2020-08-19 11:42:45] Option1...
## [1] "t0"  "t50"
## INFO [2020-08-19 11:42:45] Split table t0...
## INFO [2020-08-19 11:42:45] Done.
## [1] ""
## INFO [2020-08-19 11:42:45] No glom ...
## INFO [2020-08-19 11:42:45] Bray ...
## 
##  mutant sauvage 
##       6       6 
## INFO [2020-08-19 11:42:46] Done
## INFO [2020-08-19 11:42:46] Unifrac ...
## INFO [2020-08-19 11:42:46] Done
## INFO [2020-08-19 11:42:46] wunifrac ...
## INFO [2020-08-19 11:42:46] Done
## 
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)   
## Depth      1   0.53973 0.53973  2.8355 0.17954 0.022977 * 
## souche     1   0.75338 0.75338  3.9580 0.25061 0.004995 **
## Residuals  9   1.71311 0.19035         0.56985            
## Total     11   3.00623                 1.00000            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1  0.952842 4.640344 0.3169559   0.006      0.006   *
## 
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs  MeanSqs F.Model      R2  Pr(>F)  
## Depth      1   0.12045 0.120447  1.6362 0.12272 0.13487  
## souche     1   0.19850 0.198504  2.6965 0.20225 0.01099 *
## Residuals  9   0.66253 0.073615         0.67503          
## Total     11   0.98148                  1.00000          
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.2429196 3.289082 0.2475026   0.004      0.004   *
## 
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)   
## Depth      1   0.51694 0.51694  5.3962 0.32059 0.003996 **
## souche     1   0.23337 0.23337  2.4360 0.14472 0.059940 . 
## Residuals  9   0.86218 0.09580         0.53469            
## Total     11   1.61249                 1.00000            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
##               pairs Df SumsOfSqs  F.Model       R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.3815338 3.099498 0.236612   0.034      0.034   .
## INFO [2020-08-19 11:42:46] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1383252 
## Run 1 stress 0.1383248 
## ... New best solution
## ... Procrustes: rmse 0.0006325115  max resid 0.001417263 
## ... Similar to previous best
## Run 2 stress 0.1383252 
## ... Procrustes: rmse 0.0002779707  max resid 0.0004883743 
## ... Similar to previous best
## Run 3 stress 0.1383247 
## ... New best solution
## ... Procrustes: rmse 9.798232e-05  max resid 0.0002303137 
## ... Similar to previous best
## Run 4 stress 0.1383256 
## ... Procrustes: rmse 0.0006924759  max resid 0.001227627 
## ... Similar to previous best
## Run 5 stress 0.1383253 
## ... Procrustes: rmse 0.0004508255  max resid 0.001080641 
## ... Similar to previous best
## Run 6 stress 0.1415936 
## Run 7 stress 0.1383251 
## ... Procrustes: rmse 0.000165084  max resid 0.0003994996 
## ... Similar to previous best
## Run 8 stress 0.2147072 
## Run 9 stress 0.1416607 
## Run 10 stress 0.1471255 
## Run 11 stress 0.2163916 
## Run 12 stress 0.1416007 
## Run 13 stress 0.1415954 
## Run 14 stress 0.1415955 
## Run 15 stress 0.1415947 
## Run 16 stress 0.1415941 
## Run 17 stress 0.2308366 
## Run 18 stress 0.214706 
## Run 19 stress 0.1383248 
## ... Procrustes: rmse 5.447613e-05  max resid 0.0001091228 
## ... Similar to previous best
## Run 20 stress 0.1383249 
## ... Procrustes: rmse 0.0003649483  max resid 0.0006793969 
## ... Similar to previous best
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1383248 
## Run 1 stress 0.1415925 
## Run 2 stress 0.1415973 
## Run 3 stress 0.1415955 
## Run 4 stress 0.1383247 
## ... New best solution
## ... Procrustes: rmse 0.0002484523  max resid 0.0005541465 
## ... Similar to previous best
## Run 5 stress 0.138325 
## ... Procrustes: rmse 0.0003688702  max resid 0.0007638162 
## ... Similar to previous best
## Run 6 stress 0.1383256 
## ... Procrustes: rmse 0.0007748377  max resid 0.001373165 
## ... Similar to previous best
## Run 7 stress 0.1415974 
## Run 8 stress 0.141612 
## Run 9 stress 0.1383249 
## ... Procrustes: rmse 0.0002320819  max resid 0.0004714879 
## ... Similar to previous best
## Run 10 stress 0.1415921 
## Run 11 stress 0.1383249 
## ... Procrustes: rmse 0.0003083611  max resid 0.0005970506 
## ... Similar to previous best
## Run 12 stress 0.138327 
## ... Procrustes: rmse 0.001259103  max resid 0.002256352 
## ... Similar to previous best
## Run 13 stress 0.1383258 
## ... Procrustes: rmse 0.000839465  max resid 0.001480589 
## ... Similar to previous best
## Run 14 stress 0.1415938 
## Run 15 stress 0.1415932 
## Run 16 stress 0.1471255 
## Run 17 stress 0.2217286 
## Run 18 stress 0.1416426 
## Run 19 stress 0.2214244 
## Run 20 stress 0.1471256 
## *** Solution reached
## Run 0 stress 0.1396049 
## Run 1 stress 0.1554507 
## Run 2 stress 0.1396049 
## ... New best solution
## ... Procrustes: rmse 2.255282e-05  max resid 4.666725e-05 
## ... Similar to previous best
## Run 3 stress 0.1396049 
## ... Procrustes: rmse 2.672727e-05  max resid 5.696865e-05 
## ... Similar to previous best
## Run 4 stress 0.139605 
## ... Procrustes: rmse 0.0002209797  max resid 0.0004719349 
## ... Similar to previous best
## Run 5 stress 0.1554503 
## Run 6 stress 0.2004091 
## Run 7 stress 0.2597856 
## Run 8 stress 0.3237913 
## Run 9 stress 0.1396049 
## ... Procrustes: rmse 0.0001302444  max resid 0.0002794703 
## ... Similar to previous best
## Run 10 stress 0.3359247 
## Run 11 stress 0.139605 
## ... Procrustes: rmse 0.0001271679  max resid 0.0002570252 
## ... Similar to previous best
## Run 12 stress 0.1396049 
## ... Procrustes: rmse 2.396927e-05  max resid 5.139241e-05 
## ... Similar to previous best
## Run 13 stress 0.1554504 
## Run 14 stress 0.2265774 
## Run 15 stress 0.1396049 
## ... Procrustes: rmse 8.842138e-05  max resid 0.000186525 
## ... Similar to previous best
## Run 16 stress 0.1976767 
## Run 17 stress 0.1554504 
## Run 18 stress 0.3359627 
## Run 19 stress 0.1396049 
## ... Procrustes: rmse 0.0001482711  max resid 0.0003181528 
## ... Similar to previous best
## Run 20 stress 0.1396049 
## ... Procrustes: rmse 1.99135e-05  max resid 4.104833e-05 
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.04595665 
## Run 1 stress 0.04595686 
## ... Procrustes: rmse 4.794641e-05  max resid 0.0001040953 
## ... Similar to previous best
## Run 2 stress 0.0459568 
## ... Procrustes: rmse 4.159935e-05  max resid 0.0001000835 
## ... Similar to previous best
## Run 3 stress 0.08294085 
## Run 4 stress 0.08256847 
## Run 5 stress 0.05106926 
## Run 6 stress 0.05106757 
## Run 7 stress 0.0510631 
## Run 8 stress 0.05318672 
## Run 9 stress 0.04595582 
## ... New best solution
## ... Procrustes: rmse 0.000895018  max resid 0.002195193 
## ... Similar to previous best
## Run 10 stress 0.08256776 
## Run 11 stress 0.08157745 
## Run 12 stress 0.05318842 
## Run 13 stress 0.05318129 
## Run 14 stress 0.08157773 
## Run 15 stress 0.08157383 
## Run 16 stress 0.0483876 
## Run 17 stress 0.0825701 
## Run 18 stress 0.0837327 
## Run 19 stress 0.04595726 
## ... Procrustes: rmse 0.0004611338  max resid 0.001114228 
## ... Similar to previous best
## Run 20 stress 0.08158127 
## *** Solution reached
## INFO [2020-08-19 11:42:47] Done.
## INFO [2020-08-19 11:42:47] Saving ...
## INFO [2020-08-19 11:42:49] Supplement Beta plots ...
## INFO [2020-08-19 11:42:49] Done.
## INFO [2020-08-19 11:42:49] Split table t50...
## INFO [2020-08-19 11:42:49] Done.
## [1] ""
## INFO [2020-08-19 11:42:49] No glom ...
## INFO [2020-08-19 11:42:49] Bray ...
## 
##  mutant sauvage 
##       6       6 
## INFO [2020-08-19 11:42:49] Done
## INFO [2020-08-19 11:42:49] Unifrac ...
## INFO [2020-08-19 11:42:49] Done
## INFO [2020-08-19 11:42:49] wunifrac ...
## INFO [2020-08-19 11:42:49] Done
## 
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.06369 0.06369   3.118 0.03093 0.113886    
## souche     1   1.81185 1.81185  88.707 0.87981 0.000999 ***
## Residuals  9   0.18383 0.02043         0.08926             
## Total     11   2.05937                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1  1.817719 75.21929 0.8826557   0.003      0.003   *
## 
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.08586 0.08586   7.227 0.08985 0.006993 ** 
## souche     1   0.76280 0.76280  64.202 0.79825 0.000999 ***
## Residuals  9   0.10693 0.01188         0.11190             
## Total     11   0.95559                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.7648939 40.10959 0.8004374   0.002      0.002   *
## 
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.01031 0.01031   2.114 0.01920 0.170829    
## souche     1   0.48297 0.48297  98.982 0.89905 0.000999 ***
## Residuals  9   0.04391 0.00488         0.08175             
## Total     11   0.53720                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.4854355 93.77947 0.9036418   0.004      0.004   *
## INFO [2020-08-19 11:42:49] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 7.297422e-05 
## Run 1 stress 0.2334455 
## Run 2 stress 0.2444045 
## Run 3 stress 0.3069117 
## Run 4 stress 9.815733e-05 
## ... Procrustes: rmse 0.0002642253  max resid 0.0006085487 
## ... Similar to previous best
## Run 5 stress 9.466169e-05 
## ... Procrustes: rmse 0.0002364957  max resid 0.0005860717 
## ... Similar to previous best
## Run 6 stress 9.810564e-05 
## ... Procrustes: rmse 0.0001191326  max resid 0.0002410768 
## ... Similar to previous best
## Run 7 stress 9.54298e-05 
## ... Procrustes: rmse 8.12396e-05  max resid 0.0001787486 
## ... Similar to previous best
## Run 8 stress 9.386827e-05 
## ... Procrustes: rmse 0.0001338526  max resid 0.0002820177 
## ... Similar to previous best
## Run 9 stress 9.429826e-05 
## ... Procrustes: rmse 0.000225061  max resid 0.000565046 
## ... Similar to previous best
## Run 10 stress 9.159822e-05 
## ... Procrustes: rmse 0.0002320587  max resid 0.0005398254 
## ... Similar to previous best
## Run 11 stress 9.909955e-05 
## ... Procrustes: rmse 0.0001134436  max resid 0.0002162572 
## ... Similar to previous best
## Run 12 stress 8.915087e-05 
## ... Procrustes: rmse 7.946781e-05  max resid 0.0002197547 
## ... Similar to previous best
## Run 13 stress 9.370447e-05 
## ... Procrustes: rmse 9.325583e-05  max resid 0.0002250259 
## ... Similar to previous best
## Run 14 stress 9.683309e-05 
## ... Procrustes: rmse 7.282874e-05  max resid 0.0001491394 
## ... Similar to previous best
## Run 15 stress 9.249103e-05 
## ... Procrustes: rmse 8.132925e-05  max resid 0.0002212376 
## ... Similar to previous best
## Run 16 stress 9.346381e-05 
## ... Procrustes: rmse 8.871461e-05  max resid 0.0002397497 
## ... Similar to previous best
## Run 17 stress 8.81122e-05 
## ... Procrustes: rmse 7.927522e-05  max resid 0.0001150675 
## ... Similar to previous best
## Run 18 stress 8.411536e-05 
## ... Procrustes: rmse 0.0002189546  max resid 0.0005114147 
## ... Similar to previous best
## Run 19 stress 9.923089e-05 
## ... Procrustes: rmse 0.0002622837  max resid 0.0006037906 
## ... Similar to previous best
## Run 20 stress 0.233779 
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 8.694326e-05 
## Run 1 stress 9.7571e-05 
## ... Procrustes: rmse 2.871858e-05  max resid 5.893487e-05 
## ... Similar to previous best
## Run 2 stress 9.156634e-05 
## ... Procrustes: rmse 0.0002512312  max resid 0.0006491876 
## ... Similar to previous best
## Run 3 stress 8.967182e-05 
## ... Procrustes: rmse 6.22289e-05  max resid 0.0001626015 
## ... Similar to previous best
## Run 4 stress 9.215938e-05 
## ... Procrustes: rmse 7.398548e-05  max resid 0.0001671002 
## ... Similar to previous best
## Run 5 stress 9.377216e-05 
## ... Procrustes: rmse 0.0002407549  max resid 0.0006674717 
## ... Similar to previous best
## Run 6 stress 0.2288378 
## Run 7 stress 9.596657e-05 
## ... Procrustes: rmse 6.581733e-05  max resid 0.0001652081 
## ... Similar to previous best
## Run 8 stress 9.656764e-05 
## ... Procrustes: rmse 0.0002463088  max resid 0.0006818128 
## ... Similar to previous best
## Run 9 stress 9.542873e-05 
## ... Procrustes: rmse 2.596822e-05  max resid 5.253937e-05 
## ... Similar to previous best
## Run 10 stress 7.987219e-05 
## ... New best solution
## ... Procrustes: rmse 0.0002259861  max resid 0.0005875277 
## ... Similar to previous best
## Run 11 stress 9.787269e-05 
## ... Procrustes: rmse 0.000171325  max resid 0.0005194162 
## ... Similar to previous best
## Run 12 stress 9.546432e-05 
## ... Procrustes: rmse 0.0002346911  max resid 0.0006103371 
## ... Similar to previous best
## Run 13 stress 9.82409e-05 
## ... Procrustes: rmse 0.0001733348  max resid 0.0005260992 
## ... Similar to previous best
## Run 14 stress 9.189414e-05 
## ... Procrustes: rmse 0.0002200697  max resid 0.000611242 
## ... Similar to previous best
## Run 15 stress 9.309562e-05 
## ... Procrustes: rmse 0.0002319344  max resid 0.0006015431 
## ... Similar to previous best
## Run 16 stress 8.067644e-05 
## ... Procrustes: rmse 0.0002224298  max resid 0.0005708495 
## ... Similar to previous best
## Run 17 stress 9.738121e-05 
## ... Procrustes: rmse 0.0001826412  max resid 0.0003705574 
## ... Similar to previous best
## Run 18 stress 9.44268e-05 
## ... Procrustes: rmse 0.0001971326  max resid 0.0004378512 
## ... Similar to previous best
## Run 19 stress 7.276678e-05 
## ... New best solution
## ... Procrustes: rmse 0.0002095399  max resid 0.000564796 
## ... Similar to previous best
## Run 20 stress 9.663297e-05 
## ... Procrustes: rmse 0.000201736  max resid 0.0006023578 
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 9.633728e-05 
## Run 1 stress 9.533088e-05 
## ... New best solution
## ... Procrustes: rmse 0.000178975  max resid 0.0003245182 
## ... Similar to previous best
## Run 2 stress 0.2407971 
## Run 3 stress 8.464239e-05 
## ... New best solution
## ... Procrustes: rmse 0.0002079414  max resid 0.0006492518 
## ... Similar to previous best
## Run 4 stress 8.87606e-05 
## ... Procrustes: rmse 0.0001284124  max resid 0.0002283617 
## ... Similar to previous best
## Run 5 stress 9.574923e-05 
## ... Procrustes: rmse 0.0001637072  max resid 0.0003062904 
## ... Similar to previous best
## Run 6 stress 9.996618e-05 
## ... Procrustes: rmse 0.0001796929  max resid 0.0003230694 
## ... Similar to previous best
## Run 7 stress 9.501575e-05 
## ... Procrustes: rmse 0.0001621139  max resid 0.0002966499 
## ... Similar to previous best
## Run 8 stress 9.712025e-05 
## ... Procrustes: rmse 0.0001503913  max resid 0.0002906577 
## ... Similar to previous best
## Run 9 stress 9.514845e-05 
## ... Procrustes: rmse 0.0001581236  max resid 0.0002916892 
## ... Similar to previous best
## Run 10 stress 9.249757e-05 
## ... Procrustes: rmse 0.0001995491  max resid 0.000622048 
## ... Similar to previous best
## Run 11 stress 8.211476e-05 
## ... New best solution
## ... Procrustes: rmse 0.000135033  max resid 0.0002298485 
## ... Similar to previous best
## Run 12 stress 9.168225e-05 
## ... Procrustes: rmse 0.0001485928  max resid 0.0002177646 
## ... Similar to previous best
## Run 13 stress 8.864717e-05 
## ... Procrustes: rmse 0.0001293885  max resid 0.0002082727 
## ... Similar to previous best
## Run 14 stress 9.177623e-05 
## ... Procrustes: rmse 0.0001214138  max resid 0.0002339482 
## ... Similar to previous best
## Run 15 stress 9.530143e-05 
## ... Procrustes: rmse 0.0001100754  max resid 0.0002382435 
## ... Similar to previous best
## Run 16 stress 0.3023067 
## Run 17 stress 0.3355958 
## Run 18 stress 9.362259e-05 
## ... Procrustes: rmse 0.0001483153  max resid 0.0002641358 
## ... Similar to previous best
## Run 19 stress 9.206595e-05 
## ... Procrustes: rmse 0.0001338575  max resid 0.0002391863 
## ... Similar to previous best
## Run 20 stress 9.863644e-05 
## ... Procrustes: rmse 0.0001404396  max resid 0.0002415025 
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.001774538 
## Run 1 stress 9.93138e-05 
## ... New best solution
## ... Procrustes: rmse 0.01016925  max resid 0.01925855 
## Run 2 stress 0.004372292 
## Run 3 stress 0.0001985273 
## ... Procrustes: rmse 0.0005914205  max resid 0.001120358 
## ... Similar to previous best
## Run 4 stress 9.972073e-05 
## ... Procrustes: rmse 0.0002558317  max resid 0.0004699184 
## ... Similar to previous best
## Run 5 stress 0.0005051606 
## ... Procrustes: rmse 0.002498253  max resid 0.004586508 
## ... Similar to previous best
## Run 6 stress 0.001107762 
## Run 7 stress 0.0002511515 
## ... Procrustes: rmse 0.001004284  max resid 0.001713014 
## ... Similar to previous best
## Run 8 stress 0.0001881088 
## ... Procrustes: rmse 0.0006493462  max resid 0.001258036 
## ... Similar to previous best
## Run 9 stress 0.3298258 
## Run 10 stress 0.0003399524 
## ... Procrustes: rmse 0.001519312  max resid 0.002712545 
## ... Similar to previous best
## Run 11 stress 0.001735692 
## Run 12 stress 0.0002769074 
## ... Procrustes: rmse 0.0005731094  max resid 0.0009233264 
## ... Similar to previous best
## Run 13 stress 0.001273921 
## Run 14 stress 0.0004609634 
## ... Procrustes: rmse 0.002171964  max resid 0.004114243 
## ... Similar to previous best
## Run 15 stress 0.0002006846 
## ... Procrustes: rmse 0.0007126281  max resid 0.001307296 
## ... Similar to previous best
## Run 16 stress 0.001248139 
## Run 17 stress 0.00155523 
## Run 18 stress 0.0008136382 
## Run 19 stress 0.0003453806 
## ... Procrustes: rmse 0.001477394  max resid 0.002797753 
## ... Similar to previous best
## Run 20 stress 0.003049723 
## *** Solution reached
## INFO [2020-08-19 11:42:50] Done.
## INFO [2020-08-19 11:42:50] Saving ...

## INFO [2020-08-19 11:42:52] Supplement Beta plots ...
## INFO [2020-08-19 11:42:52] Done.
## INFO [2020-08-19 11:42:52] Global1...
## [1] ""
## INFO [2020-08-19 11:42:52] No glom ...
## INFO [2020-08-19 11:42:52] Bray ...
##      souche
## temps mutant sauvage
##   t0       6       6
##   t50      6       6
## INFO [2020-08-19 11:42:53] Done
## INFO [2020-08-19 11:42:53] Unifrac ...
## INFO [2020-08-19 11:42:53] Done
## INFO [2020-08-19 11:42:53] wunifrac ...
## INFO [2020-08-19 11:42:53] Done
## 
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1    0.5075 0.50751  3.1218 0.06845 0.020979 *  
## temps      1    2.1846 2.18458 13.4380 0.29463 0.000999 ***
## souche     1    1.4711 1.47112  9.0493 0.19841 0.000999 ***
## Residuals 20    3.2514 0.16257         0.43851             
## Total     23    7.4146                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
##                       pairs Df SumsOfSqs   F.Model        R2 p.value p.adjusted
## 1   t0-sauvage vs t0-mutant  1  0.952842  4.640344 0.3169559   0.004     0.0048
## 2 t0-sauvage vs t50-sauvage  1  2.020676 28.967360 0.7433750   0.002     0.0040
## 3  t0-sauvage vs t50-mutant  1  2.197269 26.004113 0.7222540   0.001     0.0030
## 4  t0-mutant vs t50-sauvage  1  1.680832 11.591365 0.5368519   0.006     0.0060
## 5   t0-mutant vs t50-mutant  1  1.569713  9.826226 0.4956176   0.001     0.0030
## 6 t50-sauvage vs t50-mutant  1  1.817719 75.219295 0.8826557   0.004     0.0048
##   sig
## 1   *
## 2   *
## 3   *
## 4   *
## 5   *
## 6   *
## 
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.14326 0.14326  2.4262 0.04787 0.032967 *  
## temps      1   1.03837 1.03837 17.5862 0.34698 0.000999 ***
## souche     1   0.63007 0.63007 10.6711 0.21054 0.000999 ***
## Residuals 20   1.18089 0.05904         0.39460             
## Total     23   2.99260                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
##                       pairs Df SumsOfSqs   F.Model        R2 p.value p.adjusted
## 1   t0-sauvage vs t0-mutant  1 0.2378044  3.318044 0.2491390   0.001     0.0040
## 2 t0-sauvage vs t50-sauvage  1 0.5256437 11.749607 0.5402216   0.004     0.0048
## 3  t0-sauvage vs t50-mutant  1 1.0643999 21.792240 0.6854578   0.002     0.0040
## 4  t0-mutant vs t50-sauvage  1 0.6364616 14.416735 0.5904448   0.003     0.0045
## 5   t0-mutant vs t50-mutant  1 0.8835482 18.310605 0.6467755   0.002     0.0040
## 6 t50-sauvage vs t50-mutant  1 0.7775266 36.468644 0.7848011   0.006     0.0060
##   sig
## 1   *
## 2   *
## 3   *
## 4   *
## 5   *
## 6   *
## 
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.21699 0.21699  6.4192 0.14472 0.001998 ** 
## temps      1   0.35230 0.35230 10.4221 0.23497 0.000999 ***
## souche     1   0.25400 0.25400  7.5142 0.16941 0.000999 ***
## Residuals 20   0.67606 0.03380         0.45090             
## Total     23   1.49935                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
##                       pairs Df SumsOfSqs   F.Model        R2 p.value p.adjusted
## 1   t0-sauvage vs t0-mutant  1 0.1634340  2.838825 0.2211125   0.052     0.0520
## 2 t0-sauvage vs t50-sauvage  1 0.3595478 13.063956 0.5664230   0.002     0.0060
## 3  t0-sauvage vs t50-mutant  1 0.4692174 15.571734 0.6089432   0.004     0.0060
## 4  t0-mutant vs t50-sauvage  1 0.2136786  6.791884 0.4044742   0.006     0.0072
## 5   t0-mutant vs t50-mutant  1 0.2555299  7.499838 0.4285662   0.004     0.0060
## 6 t50-sauvage vs t50-mutant  1 0.3054314 75.930639 0.8836271   0.002     0.0060
##   sig
## 1    
## 2   *
## 3   *
## 4   *
## 5   *
## 6   *
## INFO [2020-08-19 11:42:54] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764 
## Run 1 stress 0.1278079 
## Run 2 stress 0.1004882 
## ... Procrustes: rmse 0.00582239  max resid 0.02234147 
## Run 3 stress 0.127817 
## Run 4 stress 0.1282047 
## Run 5 stress 0.1004882 
## ... Procrustes: rmse 0.005801538  max resid 0.02225939 
## Run 6 stress 0.1282053 
## Run 7 stress 0.1316113 
## Run 8 stress 0.1004882 
## ... Procrustes: rmse 0.005811  max resid 0.02233573 
## Run 9 stress 0.1004882 
## ... Procrustes: rmse 0.005799686  max resid 0.02229184 
## Run 10 stress 0.138303 
## Run 11 stress 0.1004882 
## ... Procrustes: rmse 0.005810812  max resid 0.02230361 
## Run 12 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 2.234472e-06  max resid 5.37307e-06 
## ... Similar to previous best
## Run 13 stress 0.1278188 
## Run 14 stress 0.1282049 
## Run 15 stress 0.1282048 
## Run 16 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 9.679456e-06  max resid 2.799009e-05 
## ... Similar to previous best
## Run 17 stress 0.1004882 
## ... Procrustes: rmse 0.005832762  max resid 0.02235717 
## Run 18 stress 0.1004882 
## ... Procrustes: rmse 0.005822964  max resid 0.02233735 
## Run 19 stress 0.131635 
## Run 20 stress 0.1004882 
## ... Procrustes: rmse 0.005825946  max resid 0.02234164 
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882 
## Run 1 stress 0.1004882 
## ... Procrustes: rmse 2.561791e-05  max resid 8.129794e-05 
## ... Similar to previous best
## Run 2 stress 0.1004765 
## ... New best solution
## ... Procrustes: rmse 0.005817267  max resid 0.02226881 
## Run 3 stress 0.1278241 
## Run 4 stress 0.1351097 
## Run 5 stress 0.1282049 
## Run 6 stress 0.2431576 
## Run 7 stress 0.1004765 
## ... Procrustes: rmse 0.0001663419  max resid 0.0004631308 
## ... Similar to previous best
## Run 8 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 6.287301e-05  max resid 0.0001670129 
## ... Similar to previous best
## Run 9 stress 0.1004764 
## ... Procrustes: rmse 7.207364e-05  max resid 0.0001870077 
## ... Similar to previous best
## Run 10 stress 0.1316112 
## Run 11 stress 0.1278165 
## Run 12 stress 0.1004764 
## ... Procrustes: rmse 9.325252e-06  max resid 1.725324e-05 
## ... Similar to previous best
## Run 13 stress 0.1004764 
## ... Procrustes: rmse 4.245998e-05  max resid 0.0001149144 
## ... Similar to previous best
## Run 14 stress 0.1004765 
## ... Procrustes: rmse 0.0001360717  max resid 0.0004412301 
## ... Similar to previous best
## Run 15 stress 0.1322826 
## Run 16 stress 0.1332824 
## Run 17 stress 0.1004764 
## ... Procrustes: rmse 1.032627e-05  max resid 2.185507e-05 
## ... Similar to previous best
## Run 18 stress 0.1004764 
## ... Procrustes: rmse 2.070388e-05  max resid 5.152314e-05 
## ... Similar to previous best
## Run 19 stress 0.1004764 
## ... Procrustes: rmse 1.307659e-05  max resid 3.520137e-05 
## ... Similar to previous best
## Run 20 stress 0.127819 
## *** Solution reached
## Run 0 stress 0.1240701 
## Run 1 stress 0.1242994 
## ... Procrustes: rmse 0.00790244  max resid 0.03076246 
## Run 2 stress 0.1240701 
## ... New best solution
## ... Procrustes: rmse 2.43171e-06  max resid 8.415182e-06 
## ... Similar to previous best
## Run 3 stress 0.1240701 
## ... New best solution
## ... Procrustes: rmse 5.616045e-06  max resid 2.097401e-05 
## ... Similar to previous best
## Run 4 stress 0.1249445 
## Run 5 stress 0.171959 
## Run 6 stress 0.1742561 
## Run 7 stress 0.1240701 
## ... Procrustes: rmse 5.193758e-06  max resid 1.730566e-05 
## ... Similar to previous best
## Run 8 stress 0.1240701 
## ... Procrustes: rmse 8.135601e-06  max resid 2.580401e-05 
## ... Similar to previous best
## Run 9 stress 0.1240701 
## ... Procrustes: rmse 1.113284e-05  max resid 3.98475e-05 
## ... Similar to previous best
## Run 10 stress 0.125379 
## Run 11 stress 0.1249445 
## Run 12 stress 0.1240701 
## ... Procrustes: rmse 3.679917e-06  max resid 1.134398e-05 
## ... Similar to previous best
## Run 13 stress 0.1238509 
## ... New best solution
## ... Procrustes: rmse 0.01402275  max resid 0.05064204 
## Run 14 stress 0.125379 
## Run 15 stress 0.1242988 
## ... Procrustes: rmse 0.01576687  max resid 0.05033526 
## Run 16 stress 0.1249448 
## Run 17 stress 0.1240702 
## ... Procrustes: rmse 0.01402352  max resid 0.05072772 
## Run 18 stress 0.1240701 
## ... Procrustes: rmse 0.01402285  max resid 0.05072296 
## Run 19 stress 0.125379 
## Run 20 stress 0.125379 
## *** No convergence -- monoMDS stopping criteria:
##     17: stress ratio > sratmax
##      3: scale factor of the gradient < sfgrmin
## Run 0 stress 0.07641955 
## Run 1 stress 0.07642016 
## ... Procrustes: rmse 0.0005874296  max resid 0.001699592 
## ... Similar to previous best
## Run 2 stress 0.08113255 
## Run 3 stress 0.0810459 
## Run 4 stress 0.08122861 
## Run 5 stress 0.08122864 
## Run 6 stress 0.08113645 
## Run 7 stress 0.08122922 
## Run 8 stress 0.07683517 
## ... Procrustes: rmse 0.009197378  max resid 0.03779424 
## Run 9 stress 0.08122852 
## Run 10 stress 0.09530016 
## Run 11 stress 0.08122904 
## Run 12 stress 0.0764211 
## ... Procrustes: rmse 0.0004631289  max resid 0.001299319 
## ... Similar to previous best
## Run 13 stress 0.07683559 
## ... Procrustes: rmse 0.009307862  max resid 0.03846817 
## Run 14 stress 0.08074994 
## Run 15 stress 0.08074989 
## Run 16 stress 0.08074988 
## Run 17 stress 0.09858413 
## Run 18 stress 0.08122863 
## Run 19 stress 0.08104587 
## Run 20 stress 0.08122961 
## *** Solution reached
## INFO [2020-08-19 11:42:55] Done.
## INFO [2020-08-19 11:42:55] Saving ...

## INFO [2020-08-19 11:42:57] Supplement Beta plots ...
## INFO [2020-08-19 11:42:57] Done.
## INFO [2020-08-19 11:42:57] Global2...
## [1] ""
## INFO [2020-08-19 11:42:57] No glom ...
## INFO [2020-08-19 11:42:57] Bray ...
## 
##  t0 t50 
##  12  12 
## INFO [2020-08-19 11:42:57] Done
## INFO [2020-08-19 11:42:57] Unifrac ...
## INFO [2020-08-19 11:42:57] Done
## INFO [2020-08-19 11:42:57] wunifrac ...
## INFO [2020-08-19 11:42:58] Done
## 
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1    0.5075 0.50751  2.2568 0.06845 0.043956 *  
## temps      1    2.1846 2.18458  9.7144 0.29463 0.000999 ***
## Residuals 21    4.7225 0.22488         0.63692             
## Total     23    7.4146                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
##       pairs Df SumsOfSqs  F.Model       R2 p.value p.adjusted sig
## 1 t0 vs t50  1  2.348965 10.20159 0.316804   0.001      0.001  **
## 
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.14627 0.14627  1.6469 0.04771 0.166833    
## temps      1   1.05433 1.05433 11.8715 0.34392 0.000999 ***
## Residuals 21   1.86505 0.08881         0.60837             
## Total     23   3.06565                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
##       pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 t0 vs t50  1  1.064335 11.70001 0.3471812   0.001      0.001  **
## 
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.19527 0.19527  4.7847 0.14237 0.004995 ** 
## temps      1   0.31930 0.31930  7.8237 0.23279 0.000999 ***
## Residuals 21   0.85705 0.04081         0.62484             
## Total     23   1.37163                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
##       pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 t0 vs t50  1 0.3740116 8.247895 0.2726767   0.001      0.001  **
## INFO [2020-08-19 11:42:58] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764 
## Run 1 stress 0.1004764 
## ... Procrustes: rmse 1.460734e-05  max resid 4.148648e-05 
## ... Similar to previous best
## Run 2 stress 0.1278253 
## Run 3 stress 0.1322863 
## Run 4 stress 0.1282052 
## Run 5 stress 0.1332149 
## Run 6 stress 0.1004882 
## ... Procrustes: rmse 0.005834734  max resid 0.02240601 
## Run 7 stress 0.1278145 
## Run 8 stress 0.1316113 
## Run 9 stress 0.1332168 
## Run 10 stress 0.1004764 
## ... Procrustes: rmse 7.246805e-05  max resid 0.0001909761 
## ... Similar to previous best
## Run 11 stress 0.1004882 
## ... Procrustes: rmse 0.005820094  max resid 0.02233923 
## Run 12 stress 0.1332148 
## Run 13 stress 0.13229 
## Run 14 stress 0.1004764 
## ... Procrustes: rmse 1.053394e-05  max resid 2.279406e-05 
## ... Similar to previous best
## Run 15 stress 0.1274964 
## Run 16 stress 0.1004764 
## ... Procrustes: rmse 3.415503e-05  max resid 9.322872e-05 
## ... Similar to previous best
## Run 17 stress 0.1332825 
## Run 18 stress 0.1004764 
## ... Procrustes: rmse 1.31862e-05  max resid 4.616163e-05 
## ... Similar to previous best
## Run 19 stress 0.1004882 
## ... Procrustes: rmse 0.005806592  max resid 0.02229737 
## Run 20 stress 0.1278118 
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882 
## Run 1 stress 0.1278272 
## Run 2 stress 0.1004882 
## ... New best solution
## ... Procrustes: rmse 6.819145e-06  max resid 1.454001e-05 
## ... Similar to previous best
## Run 3 stress 0.1282047 
## Run 4 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 0.005815289  max resid 0.02232781 
## Run 5 stress 0.1004882 
## ... Procrustes: rmse 0.005819447  max resid 0.02236284 
## Run 6 stress 0.1282048 
## Run 7 stress 0.1004883 
## ... Procrustes: rmse 0.005841318  max resid 0.02240352 
## Run 8 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 6.266375e-05  max resid 0.0001718134 
## ... Similar to previous best
## Run 9 stress 0.1004764 
## ... Procrustes: rmse 2.719159e-06  max resid 5.941329e-06 
## ... Similar to previous best
## Run 10 stress 0.1004882 
## ... Procrustes: rmse 0.005839821  max resid 0.02239883 
## Run 11 stress 0.1282046 
## Run 12 stress 0.1004882 
## ... Procrustes: rmse 0.005807723  max resid 0.0222973 
## Run 13 stress 0.1332835 
## Run 14 stress 0.1282046 
## Run 15 stress 0.1004882 
## ... Procrustes: rmse 0.005818327  max resid 0.02232324 
## Run 16 stress 0.1004882 
## ... Procrustes: rmse 0.005804974  max resid 0.02231282 
## Run 17 stress 0.1282048 
## Run 18 stress 0.1004882 
## ... Procrustes: rmse 0.005822381  max resid 0.02233384 
## Run 19 stress 0.1282046 
## Run 20 stress 0.1004765 
## ... Procrustes: rmse 7.581458e-05  max resid 0.0001973484 
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.1256803 
## Run 1 stress 0.1249249 
## ... New best solution
## ... Procrustes: rmse 0.01684669  max resid 0.0457718 
## Run 2 stress 0.3725376 
## Run 3 stress 0.1249249 
## ... New best solution
## ... Procrustes: rmse 1.876361e-05  max resid 4.157707e-05 
## ... Similar to previous best
## Run 4 stress 0.1268287 
## Run 5 stress 0.1698976 
## Run 6 stress 0.1249249 
## ... Procrustes: rmse 3.354137e-05  max resid 9.441521e-05 
## ... Similar to previous best
## Run 7 stress 0.1249249 
## ... Procrustes: rmse 2.540111e-06  max resid 5.484595e-06 
## ... Similar to previous best
## Run 8 stress 0.1268271 
## Run 9 stress 0.1249058 
## ... New best solution
## ... Procrustes: rmse 0.01342379  max resid 0.04855629 
## Run 10 stress 0.1263106 
## Run 11 stress 0.1249058 
## ... Procrustes: rmse 1.359932e-05  max resid 3.813875e-05 
## ... Similar to previous best
## Run 12 stress 0.1262165 
## Run 13 stress 0.1249249 
## ... Procrustes: rmse 0.0134237  max resid 0.04848362 
## Run 14 stress 0.1268278 
## Run 15 stress 0.1249058 
## ... Procrustes: rmse 7.025107e-06  max resid 1.214134e-05 
## ... Similar to previous best
## Run 16 stress 0.1249058 
## ... Procrustes: rmse 2.572412e-05  max resid 8.542448e-05 
## ... Similar to previous best
## Run 17 stress 0.1249058 
## ... Procrustes: rmse 1.454983e-05  max resid 3.567124e-05 
## ... Similar to previous best
## Run 18 stress 0.1263106 
## Run 19 stress 0.1262165 
## Run 20 stress 0.1249058 
## ... New best solution
## ... Procrustes: rmse 5.713133e-06  max resid 1.702337e-05 
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.08612709 
## Run 1 stress 0.08612721 
## ... Procrustes: rmse 0.0001230758  max resid 0.0005015536 
## ... Similar to previous best
## Run 2 stress 0.0861272 
## ... Procrustes: rmse 6.26278e-05  max resid 0.0002270132 
## ... Similar to previous best
## Run 3 stress 0.08612712 
## ... Procrustes: rmse 6.316098e-05  max resid 0.0002613876 
## ... Similar to previous best
## Run 4 stress 0.1284577 
## Run 5 stress 0.2311373 
## Run 6 stress 0.0861274 
## ... Procrustes: rmse 9.682461e-05  max resid 0.0003439576 
## ... Similar to previous best
## Run 7 stress 0.207305 
## Run 8 stress 0.09418535 
## Run 9 stress 0.08612712 
## ... Procrustes: rmse 3.100432e-05  max resid 9.834706e-05 
## ... Similar to previous best
## Run 10 stress 0.09418486 
## Run 11 stress 0.09418437 
## Run 12 stress 0.1284591 
## Run 13 stress 0.09344909 
## Run 14 stress 0.08612709 
## ... Procrustes: rmse 2.062e-05  max resid 8.298303e-05 
## ... Similar to previous best
## Run 15 stress 0.1312055 
## Run 16 stress 0.09345045 
## Run 17 stress 0.09418426 
## Run 18 stress 0.2310506 
## Run 19 stress 0.1290919 
## Run 20 stress 0.1295872 
## *** Solution reached
## INFO [2020-08-19 11:42:58] Done.
## INFO [2020-08-19 11:42:58] Saving ...

## INFO [2020-08-19 11:43:01] Supplement Beta plots ...
## INFO [2020-08-19 11:43:01] Done.
## INFO [2020-08-19 11:43:01] Global3...
## [1] ""
## INFO [2020-08-19 11:43:01] No glom ...
## INFO [2020-08-19 11:43:01] Bray ...
## 
##  mutant sauvage 
##      12      12 
## INFO [2020-08-19 11:43:01] Done
## INFO [2020-08-19 11:43:01] Unifrac ...
## INFO [2020-08-19 11:43:01] Done
## INFO [2020-08-19 11:43:01] wunifrac ...
## INFO [2020-08-19 11:43:01] Done
## 
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1    0.5075 0.50751  1.9574 0.06845 0.073926 .  
## souche     1    1.4622 1.46217  5.6393 0.19720 0.000999 ***
## Residuals 21    5.4449 0.25928         0.73435             
## Total     23    7.4146                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1  1.529137 5.715979 0.2062341   0.001      0.001  **
## 
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)   
## Depth      1   0.14451 0.14451  1.3558 0.04797 0.231768   
## souche     1   0.62942 0.62942  5.9052 0.20895 0.003996 **
## Residuals 21   2.23836 0.10659         0.74308            
## Total     23   3.01229                 1.00000            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
##               pairs Df SumsOfSqs F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.6656014 6.23995 0.2209618   0.002      0.002   *
## 
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs  MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.11860 0.118601  3.0286 0.10077 0.012987 *  
## souche     1   0.23596 0.235957  6.0253 0.20048 0.000999 ***
## Residuals 21   0.82238 0.039161         0.69875             
## Total     23   1.17694                  1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.2365825 5.534929 0.2010148   0.001      0.001  **
## INFO [2020-08-19 11:43:01] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764 
## Run 1 stress 0.1004764 
## ... Procrustes: rmse 1.154521e-05  max resid 3.53991e-05 
## ... Similar to previous best
## Run 2 stress 0.1004885 
## ... Procrustes: rmse 0.005819579  max resid 0.02231832 
## Run 3 stress 0.1004882 
## ... Procrustes: rmse 0.005803715  max resid 0.02229188 
## Run 4 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 1.111054e-05  max resid 2.655167e-05 
## ... Similar to previous best
## Run 5 stress 0.1004764 
## ... Procrustes: rmse 5.750598e-05  max resid 0.0001510157 
## ... Similar to previous best
## Run 6 stress 0.1004883 
## ... Procrustes: rmse 0.005849704  max resid 0.02241124 
## Run 7 stress 0.1004764 
## ... Procrustes: rmse 1.056377e-05  max resid 2.600251e-05 
## ... Similar to previous best
## Run 8 stress 0.1282046 
## Run 9 stress 0.1004882 
## ... Procrustes: rmse 0.005812432  max resid 0.02230539 
## Run 10 stress 0.1004882 
## ... Procrustes: rmse 0.005822984  max resid 0.02233264 
## Run 11 stress 0.1282051 
## Run 12 stress 0.1004765 
## ... Procrustes: rmse 0.0001189496  max resid 0.0003012801 
## ... Similar to previous best
## Run 13 stress 0.1004883 
## ... Procrustes: rmse 0.005814722  max resid 0.02230269 
## Run 14 stress 0.1004882 
## ... Procrustes: rmse 0.005815698  max resid 0.02231601 
## Run 15 stress 0.127582 
## Run 16 stress 0.1004765 
## ... Procrustes: rmse 0.0001249548  max resid 0.0003239086 
## ... Similar to previous best
## Run 17 stress 0.1004764 
## ... Procrustes: rmse 2.345706e-05  max resid 6.490324e-05 
## ... Similar to previous best
## Run 18 stress 0.1004882 
## ... Procrustes: rmse 0.005817773  max resid 0.02231451 
## Run 19 stress 0.1004882 
## ... Procrustes: rmse 0.005824295  max resid 0.02233978 
## Run 20 stress 0.1332828 
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882 
## Run 1 stress 0.1004765 
## ... New best solution
## ... Procrustes: rmse 0.005816619  max resid 0.0222787 
## Run 2 stress 0.1278281 
## Run 3 stress 0.1004765 
## ... New best solution
## ... Procrustes: rmse 2.684107e-05  max resid 7.043686e-05 
## ... Similar to previous best
## Run 4 stress 0.1004882 
## ... Procrustes: rmse 0.005827093  max resid 0.02231957 
## Run 5 stress 0.1322818 
## Run 6 stress 0.127607 
## Run 7 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 7.477728e-05  max resid 0.0001950756 
## ... Similar to previous best
## Run 8 stress 0.1004882 
## ... Procrustes: rmse 0.00581747  max resid 0.02233454 
## Run 9 stress 0.1004765 
## ... Procrustes: rmse 6.324246e-05  max resid 0.00016833 
## ... Similar to previous best
## Run 10 stress 0.1004882 
## ... Procrustes: rmse 0.005799534  max resid 0.02228797 
## Run 11 stress 0.1282046 
## Run 12 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 1.49946e-05  max resid 4.893117e-05 
## ... Similar to previous best
## Run 13 stress 0.1004765 
## ... Procrustes: rmse 0.0001098263  max resid 0.0002917477 
## ... Similar to previous best
## Run 14 stress 0.138419 
## Run 15 stress 0.1004764 
## ... Procrustes: rmse 1.039399e-05  max resid 1.978762e-05 
## ... Similar to previous best
## Run 16 stress 0.1282045 
## Run 17 stress 0.1004765 
## ... Procrustes: rmse 8.70372e-05  max resid 0.0002393608 
## ... Similar to previous best
## Run 18 stress 0.1004764 
## ... Procrustes: rmse 2.25671e-05  max resid 5.544975e-05 
## ... Similar to previous best
## Run 19 stress 0.1004764 
## ... Procrustes: rmse 1.649469e-05  max resid 3.365621e-05 
## ... Similar to previous best
## Run 20 stress 0.1004764 
## ... Procrustes: rmse 7.09353e-06  max resid 1.812244e-05 
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.1237948 
## Run 1 stress 0.1250086 
## Run 2 stress 0.1242106 
## ... Procrustes: rmse 0.0159052  max resid 0.04636032 
## Run 3 stress 0.1242106 
## ... Procrustes: rmse 0.01594085  max resid 0.04633947 
## Run 4 stress 0.1242184 
## ... Procrustes: rmse 0.009186847  max resid 0.03462367 
## Run 5 stress 0.1250086 
## Run 6 stress 0.1249325 
## Run 7 stress 0.1252029 
## Run 8 stress 0.1703687 
## Run 9 stress 0.1237948 
## ... New best solution
## ... Procrustes: rmse 0.0001119318  max resid 0.0004210171 
## ... Similar to previous best
## Run 10 stress 0.1690862 
## Run 11 stress 0.1237948 
## ... Procrustes: rmse 9.501374e-05  max resid 0.0003572056 
## ... Similar to previous best
## Run 12 stress 0.1250086 
## Run 13 stress 0.1250086 
## Run 14 stress 0.3458484 
## Run 15 stress 0.1242106 
## ... Procrustes: rmse 0.01592723  max resid 0.04638958 
## Run 16 stress 0.1242188 
## ... Procrustes: rmse 0.009446607  max resid 0.03574283 
## Run 17 stress 0.1252047 
## Run 18 stress 0.1250352 
## Run 19 stress 0.1237849 
## ... New best solution
## ... Procrustes: rmse 0.01244131  max resid 0.04504723 
## Run 20 stress 0.1242184 
## ... Procrustes: rmse 0.01531351  max resid 0.04395022 
## *** No convergence -- monoMDS stopping criteria:
##     20: stress ratio > sratmax
## Run 0 stress 0.08622534 
## Run 1 stress 0.08622531 
## ... New best solution
## ... Procrustes: rmse 0.0001078813  max resid 0.0002978303 
## ... Similar to previous best
## Run 2 stress 0.09017176 
## Run 3 stress 0.08622528 
## ... New best solution
## ... Procrustes: rmse 0.0001558791  max resid 0.0004687973 
## ... Similar to previous best
## Run 4 stress 0.09017233 
## Run 5 stress 0.1315239 
## Run 6 stress 0.1315249 
## Run 7 stress 0.09017144 
## Run 8 stress 0.3842931 
## Run 9 stress 0.08622526 
## ... New best solution
## ... Procrustes: rmse 0.0001201927  max resid 0.000340439 
## ... Similar to previous best
## Run 10 stress 0.1251127 
## Run 11 stress 0.09017141 
## Run 12 stress 0.08622542 
## ... Procrustes: rmse 0.0001732654  max resid 0.0007008996 
## ... Similar to previous best
## Run 13 stress 0.08622525 
## ... New best solution
## ... Procrustes: rmse 3.245498e-05  max resid 9.169185e-05 
## ... Similar to previous best
## Run 14 stress 0.08622524 
## ... New best solution
## ... Procrustes: rmse 4.75078e-05  max resid 0.000167523 
## ... Similar to previous best
## Run 15 stress 0.09097867 
## Run 16 stress 0.1251195 
## Run 17 stress 0.1287963 
## Run 18 stress 0.08622524 
## ... Procrustes: rmse 2.231465e-05  max resid 8.17788e-05 
## ... Similar to previous best
## Run 19 stress 0.09017212 
## Run 20 stress 0.09097896 
## *** Solution reached
## INFO [2020-08-19 11:43:02] Done.
## INFO [2020-08-19 11:43:02] Saving ...

## INFO [2020-08-19 11:43:05] Supplement Beta plots ...
## INFO [2020-08-19 11:43:05] Done.
## INFO [2020-08-19 11:43:05] Finish